{"id":"W2082090599","doi":"10.1260/0957-4565.41.10.29","title":"A New Method of Nonlinear Feature Extraction for Multi-Fault Diagnosis of Rotor Systems","year":2010,"lang":"en","type":"article","venue":"Noise & Vibration Worldwide","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministry of Transportation of Ontario","funders":"","keywords":"Feature extraction; Rotor (electric); Fault (geology); Pattern recognition (psychology); Nonlinear system; Nonlinear dimensionality reduction; Artificial intelligence; Feature (linguistics); Computer science; Feature vector; Control theory (sociology); Engineering; Dimensionality reduction","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005194537,0.000915094,0.0008801729,0.001555149,0.00040308,0.0005530914,0.000789627,0.0008967205,0.001863194],"category_scores_gemma":[0.001382003,0.0002915866,0.0009732039,0.001349996,0.0004882239,0.001446285,0.0005939382,0.0009929319,0.000994096],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003748274,"about_ca_system_score_gemma":0.000479081,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001326481,"about_ca_topic_score_gemma":0.00142619,"domain_scores_codex":[0.9993126,0.00009211297,0.00005767268,0.0002255351,0.000264378,0.00004773629],"domain_scores_gemma":[0.9994847,0.0001484647,0.00006617362,0.00007930194,0.0002050263,0.00001640623],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001275728,0.00008503979,0.001014518,0.0002708245,0.00008739901,0.0002214224,0.0001454032,0.03366328,0.1043479,0.006782379,0.00269073,0.8505635],"study_design_scores_gemma":[0.00002152485,0.00017231,0.003317076,0.00002558745,0.00005089312,0.000641951,0.00004586839,0.9357144,0.04372738,0.004788449,0.01142232,0.00007214583],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003031265,0.0001502974,0.9959697,0.00004118118,0.00003943223,0.00002569092,0.00003831567,0.0003546016,0.0003495491],"genre_scores_gemma":[0.1485264,0.000425939,0.8470628,0.00008777568,0.00008699764,0.0001569149,0.0003143961,0.000117772,0.00322093],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001863194,"threshold_uncertainty_score":0.006232977,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01533827201563125,"score_gpt":0.3337773644672433,"score_spread":0.318439092451612,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}